Evaluation of models generated from objects in video

ABSTRACT

Models are generated from objects identified in video. Each model is evaluated based on knowledge of the objects determined from video analysis, and preferred models are identified based on the evaluations. In some examples, each model could be evaluated by tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/355,285, filed Jan. 20, 2012, entitled “EVALUATION OF MODELSGENERATED FROM OBJECTS IN VIDEO,” and claims the benefit of U.S.provisional application entitled “DESCRIPTORS BASED OBJECT DETECTION”having Ser. No. 61/434,736 filed on Jan. 20, 2011. The disclosures ofthe above are incorporated herein by reference in their entireties.

TECHNICAL FIELD

Aspects of the invention are related, in general, to the field of imageprocessing and analysis.

TECHNICAL BACKGROUND

Image analysis involves performing processes on images or video in orderto identify and extract meaningful information from the images or video.In many cases, these processes are performed on digital images usingdigital image processing techniques. Computers are frequently used forperforming this analysis because large amounts of data and complexcomputations may be involved. Many image processing techniques aredesigned to emulate recognition or identification processes which occurthrough human visual perception and cognitive processing.

OVERVIEW

A method of operating an image processing system is disclosed. Themethod comprises generating models from objects identified in video. Themethod further comprises evaluating each model based on knowledge of theobjects determined from video analysis, and identifying at least onepreferred model based on the evaluating.

In an embodiment, one or more computer readable media have storedthereon program instructions which, when executed by a processingsystem, direct the processing system to generate models from objectsidentified in video. The program instructions further direct theprocessing system to perform evaluations on each model based onknowledge of the objects determined from video analysis, and identify atleast one preferred model based on the evaluations.

In an embodiment, an image processing system comprises a processingsystem. The processing system is configured to generate models fromobjects identified in video. The processing system is further configuredto perform evaluations on each model based on knowledge of the objectsdetermined from video analysis, and identify at least one preferredmodel based on the evaluations.

In an embodiment, evaluating each model based on knowledge of theobjects determined from video analysis comprises tracking a movement ofeach object in the video.

In an embodiment, tracking the movement of each object in the videocomprises using each model to track the object from which it wasgenerated.

In an embodiment, evaluating each model based on knowledge of theobjects determined from video analysis comprises evaluating an abilityof each model to identify the objects in the video that are similar tothe object from which it was generated.

In an embodiment, evaluating each model based on knowledge of theobjects determined from video analysis comprises determining an amountof false identifications made by each model of different objects indifferent video that does not include the object from which it wasgenerated.

In an embodiment, evaluating each model based on knowledge of theobjects determined from video analysis comprises tracking a movement ofeach object in the video by using each model to track the object fromwhich it was generated, evaluating an ability of each model to identifythe objects in the video that are similar to the object from which itwas generated, and determining an amount of false identifications madeby each model of different objects in different video that does notinclude the object from which it was generated.

In an embodiment, identifying at least one preferred model based on theevaluations comprises identifying a model having a greatest ability toidentify the objects in the video that are similar to the object fromwhich it was generated and having a least amount of falseidentifications of the different objects in the different video.

In an embodiment, the objects are identified in the video by manualidentification.

In an embodiment, the objects are identified in the video by human headdetection.

In an embodiment, the objects identified in the video comprise humanbody parts.

This Overview is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. It should be understood that this Overview is not intendedto identify key features or essential features of the claimed subjectmatter, nor is it intended to be used to limit the scope of the claimedsubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates an imaging system;

FIG. 2 is a flow diagram of a process according to an embodiment of theinvention for operating an image processing system;

FIG. 3 is a block diagram that illustrates video and models generatedfrom objects identified in the video;

FIG. 4 is a block diagram that illustrates video and an evaluation of amodel based on knowledge of an object in the video;

FIG. 5 is a block diagram that illustrates video and an evaluation ofmodels based on knowledge of objects in the video;

FIG. 6 is a block diagram that illustrates video and an evaluation ofmodels based on knowledge of objects in the video;

FIG. 7 is a block diagram that illustrates an image processing system.

DETAILED DESCRIPTION

The following description and associated drawings teach the best mode ofthe invention. For the purpose of teaching inventive principles, someconventional aspects of the best mode may be simplified or omitted. Thefollowing claims specify the scope of the invention. Some aspects of thebest mode may not fall within the scope of the invention as specified bythe claims. Thus, those skilled in the art will appreciate variationsfrom the best mode that fall within the scope of the invention. Thoseskilled in the art will appreciate that the features described below canbe combined in various ways to form multiple variations of theinvention. As a result, the invention is not limited to the specificexamples described below, but only by the claims and their equivalents.

Disclosed herein are systems and methods for evaluating models generatedfrom objects identified in video. Generally, a descriptors-baseddetection technique is employed to detect and identify objects using oneor more of an object's parts. Models of the object are generated andthen portions of images in video are compared to these predeterminedmodels. Preferred models are selected intelligently based on theirability to maximize the detection rate of similar objects while keepingfalse detections to a minimum.

FIGS. 1-2 are provided to illustrate one implementation of an imagingsystem 100 and its operation. FIG. 1 depicts elements of imaging system100, while FIG. 2 illustrates process 200 that describes the operationof imaging system 100.

Referring now to FIG. 1, a block diagram is shown that illustratesimaging system 100. Imaging system 100 comprises video source 101 andimage processing system 120.

Video source 101 may comprise any device having the capability tocapture video or images. Video source 101 comprises circuitry and aninterface for transmitting video or images. Video source 101 may be adevice which performs the initial optical capture of video, may be anintermediate video transfer device, or may be another type of videotransmission device. For example, video source 101 may be a videocamera, still camera, internet protocol (IP) camera, video switch, videobuffer, video server, or other video transmission device, includingcombinations thereof.

Image processing system 120 may comprise any device for processing oranalyzing video, video streams, or images. Image processing system 120comprises processing circuitry and an interface for receiving video.Image processing system 120 is capable of performing one or moreprocesses on the video streams received from video source 101. Theprocesses performed on the video may include viewing, storing,transforming, mathematical computations, modifications, objectidentification, analytical processes, conditioning, other processes, orcombinations thereof. Image processing system 120 may also compriseadditional interfaces for transmitting or receiving video streams, auser interface, memory, software, communication components, a powersupply, or structural support. Image processing system 120 may be avideo analytics system, server, digital signal processor, computingsystem, or some other type of processing device, including combinationsthereof.

Video source 101 and image processing system 120 communicate via one ormore links which may use any of a variety of communication media, suchas air, metal, optical fiber, or any other type of signal propagationpath, including combinations thereof. The links may use any of a varietyof communication protocols, such as internet, telephony, opticalnetworking, wireless communication, wireless fidelity, or any othercommunication protocols and formats, including combinations thereof. Thelink between video source 101 and image processing system 120 may bedirect as illustrated or may be indirect and accomplished using othernetworks or intermediate communication devices.

It should be understood that imaging system 100 may contain additionalvideo sources, additional image processing systems, or other devices.

Turning now to FIG. 2, process 200 describes the operation of imagingsystem 100 in an implementation, and in particular, the operation ofimage processing system 120. The steps of process 200 are indicatedbelow parenthetically.

To begin, models are generated from objects identified in video (201).In some examples, the models could be generated by scanning through thevideo and identifying marked locations in the video to create models ofthose locations. For example, the marked locations in the video couldcomprise objects that are identified in the video by manualidentification, such as by a user manually marking the portions of thevideo associated with the target objects. In some examples, the objectsidentified in the video comprise human body parts, such as human heads.In this case, the objects could be identified in the video by human headdetection and/or facial recognition, and a different model could begenerated for each human head identified in the video. In yet anotherexample, image processing system 120 could identify portions of thevideo that exhibit movement and identify the objects in the video thatare associated with that movement. Other techniques of identifyingobjects in video from which to generate models are possible and withinthe scope of this disclosure.

Once the models are generated, each model is evaluated based onknowledge of the objects determined from video analysis (203). In someexamples, to evaluate each model, image processing system 120 couldanalyze the video in order to track movement of each object in thevideo. For example, image processing system 120 could track the movementof each object in the video by using each model to track the object fromwhich it was generated. In other words, this model evaluation techniquetests the model's ability to track its associated object from which itwas generated as the object moves and changes position in the video. Forexample, in the case of modeling human heads, a movement profile foreach human could be generated based on each head model tracking themovement of its respective human through a video scene. Such trackingcould provide statistics about the dynamics of the scene, such asaverage and maximum step size of each person, rates of speed, where mostfoot traffic occurs, and the like. Such motion dynamics could be storedin association with their respective models for later use in identifyingdifferent objects, such as the heads of different humans, which mightappear in different video.

Additionally or alternatively, in some examples image processing system120 could evaluate each model based on knowledge of the objectsdetermined from video analysis by evaluating an ability of each model toidentify the objects in the video that are similar to the object fromwhich it was generated. In this evaluation, each model is tested todetermine its ability to detect and identify objects that are similar tothe object from which it was modeled. For example, continuing the aboveexample of human head modeling, each head model could be evaluatedagainst video of other humans to see which of the other humans werecorrectly identified using the head models from different humans. Insome examples, image processing system 120 could optionally determinewhich head models incorrectly detected body parts other than headsand/or other non-human objects as human heads.

Additionally or alternatively, in some examples image processing system120 could also optionally evaluate each model by determining an amountof false identifications made by each model of different objects indifferent video that does not include the object from which it wasgenerated. For example, images that do not contain any objects that wereused to generate the models in Step 201 could be analyzed using thosemodels. Any detection by the models is therefore incorrect andrepresents a false detection. For example, in the case of human headdetection, models of different heads could be compared against videothat contains no images of humans whatsoever to determine if any of themodels falsely identify other objects appearing in the video as humanheads.

Once the models are evaluated, image processing system 120 identifies atleast one preferred model based on the evaluations (205). Typically,preferred models are selected based on some criteria, such as the mostgeneral models evaluated. For example, one approach to identifyingpreferred models could comprise selecting the model that detected thegreatest number of objects in the video that are similar to the objectfrom which it was generated, then removing those objects that itdetected from the analysis, and selecting another model that detectedthe next greatest number of this same type of object in the video fromamong the remaining objects that were undetected by the first selectedmodel, and so on. This approach would ensure that the preferred modelsidentified have the best ability to generalize, but also avoidsresemblance and redundancy among the preferred models. In one example,identifying at least one preferred model based on the evaluationscomprises identifying a model having a greatest ability to identify theobjects in the video that are similar to the object from which it wasgenerated and having a least amount of false identifications ofdifferent objects in different video. In some examples, the top fivepercent of the models which created the most false detections could bedisqualified on the basis that they describe a feature that is toogeneral and might be very common in most video scenes. Other techniquesand criteria could be utilized to identify preferred models based on theevaluations and are within the scope of this disclosure.

Advantageously, using the above techniques, models of various objectsappearing in video can be evaluated to determine preferred models thatbest detect similar objects in other video. The preferred models can beselected intelligently in order to maximize the detection rate whilekeeping false detections and the number of models to a minimum. In thismanner, inferior models that are inaccurate and overly general arefiltered out and eliminated so that a smaller collection of preferred,optimal models are identified and selected for use.

FIG. 3 is a block diagram that illustrates video 300 and models 311 and312 generated from objects 301 and 302 identified in the video 300. Asshown in FIG. 3, the image displayed of video 300 shows two triangleobjects labeled 301 and 302. Although basic, two-dimensional shapes areused herein for the purpose of clarity, one of skill in the art willunderstand that much more complex objects appearing in video could bemodeled, including three-dimensional objects and portions of largerobjects, such as body parts of a human being, for example.

The objects 301 and 302 have associated models 311 and 312,respectively, that are generated from the objects 301 and 302 identifiedin the video. In this example, a user has previously marked objects 301and 302 in video 300 by designating the area in the video 300 in whichthe objects 301 and 302 appear in order to identify the objects 301 and302 in the video 300, but other object identification techniques arepossible. Based on the objects 301 and 302 identified in the video 300,respective models 311 and 312 have been generated. As shown by thedashed arrows in FIG. 3, model 311 corresponds to object 301, and model312 corresponds to object 302.

FIG. 4 is a block diagram that illustrates video 400 and an evaluationof a model 311 based on knowledge of an object 301 in the video 400. Inthis example, video 400 depicts a scene in which triangle object 301 istraveling in motion. Model 311, which was generated from object 301previously based on video 300 of FIG. 3, is used to track the movementof object 301 throughout the video scene 400. In other words, triangleobject 301 is being detected and tracked using its own model 311. Inthis example, model 311 successfully tracks the movement of object 301from which it was generated.

FIG. 5 is a block diagram that illustrates video 500 and an evaluationof models 311 and 312 based on knowledge of objects 301 and 302 in thevideo 500. This evaluation tests the ability of each model 311 and 312to detect and identify objects 302 and 301 that are similar to theobjects 301 and 302 that were used to generate their respective models311 and 312. For example, since model 311 was generated from triangleobject 301, model 311 is evaluated to determine its ability to detectsimilar triangle object 302 in video 500. Likewise, triangle model 312was modeled after triangle object 302, so the ability of model 312 todetect similar triangle object 301 is tested.

In this example, each model 311 and 312 successfully identifies asimilar object 302 and 301, respectively. Thus, as shown by the dashedarrows on FIG. 5, model 311 correctly identifies triangle object 302that is similar to triangle object 301 from which model 311 wasgenerated. Likewise, model 312 accurately identifies triangle object 301that is similar to triangle object 302 from which model 312 wasgenerated.

FIG. 6 is a block diagram that illustrates video 600 and an evaluationof models 311 and 312 based on knowledge of objects 601 and 602 in thevideo 600. In this example, although both models 311 and 312 weremodeled after triangle objects 301 and 302 as discussed above withrespect to FIG. 3, the image in the video 600 does not contain anytriangle objects. Instead, video 600 contains a circular object 601 anda square object 602. Models 311 and 312 are thus evaluated against thescene in video 600 to determine if either model 311 or 312 falselyidentifies one of the objects 601 or 602 as a triangle object.

In this example, model 311 successfully avoids falsely identifyingeither object 601 or 602 as a triangle object. However, as shown in FIG.6, model 312 falsely identifies the square object 602 as a triangleobject. Since video 600 is known to not contain any triangle objectswhatsoever, the detection of object 602 by model 312 is incorrect andrepresents a false detection. Such information could be subsequentlyused to identify preferred models, such as by eliminating model 312 forbeing too generalized and instead selecting model 311 for its superiorability to avoid false detections.

FIG. 7 illustrates image processing system 700. Image processing system700 provides an example of image processing system 120, but imageprocessing system 120 could have alternative configurations. Imageprocessing system 700 and the associated description below are intendedto provide a brief, general description of a suitable computingenvironment in which process 200 of FIG. 2 may be implemented. Manyother configurations of computing devices and software computing systemsmay be employed to implement process 200.

Image processing system 700 may be any type of computing system capableof evaluating models generated from objects identified in video, such asa client computer, server computer, internet apparatus, or anycombination or variation thereof. Image processing system 700 may beimplemented as a single computing system, but may also be implemented ina distributed manner across multiple computing systems. Image processingsystem 700 is provided as an example of a general purpose computingsystem that, when implementing process 200, becomes a specialized systemcapable of evaluating models generated from objects identified in videoand identifying preferred models based on the evaluations.

Image processing system 700 includes communication interface 710 andprocessing system 720. Processing system 720 and communication interface710 are in communication through a communication link. Processing system720 includes processor 721 and memory system 722. Memory system 722stores software 723, which, when executed by processing system 720,directs image processing system 700 to operate as described herein forprocess 200.

Communication interface 710 includes network interface 712, input ports716, and output ports 718. Communication interface 710 includescomponents that communicate over communication links, such as networkcards, ports, RF transceivers, processing circuitry and software, orsome other communication device. Communication interface 710 may beconfigured to communicate over metallic, wireless, or optical links.Communication interface 710 may be configured to use TDM, IP, Ethernet,optical networking, wireless protocols, communication signaling, or someother communication format, including combinations thereof. Imageprocessing system 700 may include multiple network interfaces.

Network interface 712 is configured to connect to external devices overnetwork 770. Network interface 712 may be configured to communicate in avariety of protocols. Input ports 716 are configured to connect to inputdevices 780 such as a video source, a storage system, a keyboard, amouse, a user interface, or other input device. Output ports 718 areconfigured to connect to output devices 790 such as a storage system,other communication links, a display, or other output devices.

Processing system 720 includes processor 721 and memory system 722.Processor 721 includes microprocessor or other circuitry that retrievesand executes operating software from memory system 722. Processor 721may comprise a single device or could be distributed across multipledevices—including devices in different geographic areas. Processor 721may be embedded in various types of equipment.

Memory system 722 may comprise any storage media readable by processingsystem 720 and capable of storing software 723, including operatingsystem 724, applications 725, model creation module 728, and modeltesting module 729. Memory system 722 may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information, such as computer readableinstructions, data structures, program modules, or other data. Memorysystem 722 may comprise a single device or could be distributed acrossmultiple devices—including devices in different geographic areas. Memorysystem 722 may be embedded in various types of equipment. Memory system722 may comprise additional elements, such as a controller, capable ofcommunicating with processing system 720.

Examples of storage media include random access memory, read onlymemory, magnetic disks, optical disks, and flash memory, as well as anycombination or variation thereof, or any other type of storage media. Insome implementations, the storage media may be a non-transitory storagemedia. In some implementations, at least a portion of the storage mediamay be transitory. It should be understood that in no case is thestorage media a propagated signal or carrier wave.

Software 723, including model creation module 728 and model testingmodule 729 in particular, comprises computer program instructions,firmware, or some other form of machine-readable processing instructionshaving process 200 embodied therein. Model creation module 728 and modeltesting module 729 may be implemented as a single application but alsoas multiple applications. Model creation module 728 and model testingmodule 729 may be stand-alone applications but may also be implementedwithin other applications distributed on multiple devices, including butnot limited to program application software and operating systemsoftware.

In general, software 723 may, when loaded into processing system 720 andexecuted, transform processing system 720, and image processing system700 overall, from a general-purpose computing system into aspecial-purpose computing system customized to evaluate models generatedfrom objects identified in video and identify preferred models based onthe evaluations as described by process 200 and its associateddiscussion.

Software 723, and model creation module 728 and model testing module 729in particular, may also transform the physical structure of memorysystem 722. The specific transformation of the physical structure maydepend on various factors in different implementations of thisdescription. Examples of such factors may include, but are not limitedto, the technology used to implement the storage media of memory system722, whether the computer-storage media are characterized as primary orsecondary storage, and the like.

For example, if the computer-storage media are implemented assemiconductor-based memory, software 723, and model creation module 728and model testing module 729 in particular, may transform the physicalstate of the semiconductor memory when the software is encoded therein.For example, software 723 may transform the state of transistors,capacitors, or other discrete circuit elements constituting thesemiconductor memory. A similar transformation may occur with respect tomagnetic or optical media. Other transformations of physical media arepossible without departing from the scope of the present description,with the foregoing examples provided only to facilitate this discussion.

Software 723 comprises operating system 724, applications 725, modelcreation module 728, and model testing module 729. Software 723 may alsocomprise additional computer programs, firmware, or some other form ofnon-transitory, machine-readable processing instructions. When executedby processing system 720, operating software 723 directs processingsystem 720 to operate image processing system 700 as described hereinfor image processing system 120 and process 200. In particular,operating software 723 directs processing system 720 to generate modelsfrom objects identified in video. Operating software 723 also directsprocessing system 720 to perform evaluations on each model based onknowledge of the objects determined from video analysis. Further,operating software 723 directs processing system 720 to identify atleast one preferred model based on the evaluations.

In this example, operating software 723 comprises a model creationsoftware module 728 that generates models from objects identified invideo. Additionally, operating software 723 comprises a model testingsoftware module 729 that performs evaluations on each model based onknowledge of the objects determined from video analysis and identifiesat least one preferred model based on the evaluations.

The above description and associated figures teach the best mode of theinvention. The following claims specify the scope of the invention. Notethat some aspects of the best mode may not fall within the scope of theinvention as specified by the claims. Those skilled in the art willappreciate that the features described above can be combined in variousways to form multiple variations of the invention. As a result, theinvention is not limited to the specific embodiments described above,but only by the following claims and their equivalents.

1. A method of operating an image processing system, the methodcomprising: generating respective models from identified objects in afirst video, wherein said respective models comprise descriptors ofparts of said identified objects; evaluating an ability of a respectivemodel to identify other objects in said first video when said otherobjects are similar to a respective identified object from which saidrespective model was generated; and identifying at least one preferredmodel based on the evaluating.
 2. The method of claim 1 whereinevaluating an ability of each respective model comprises evaluatingknowledge of the objects determined from video analysis of trackedmovement of said respective identified object in the video.
 3. Themethod of claim 1 wherein evaluating an ability of each respective modelcomprises evaluating knowledge of the objects comprising a number offalse identifications made by each model of different objects indifferent video that does not include the respective identified objectfrom which the respective model was generated.
 4. The method of claim 1wherein evaluating each model comprises evaluating knowledge of theidentified objects determined from a video analysis that comprises:tracking a movement in the video by using each respective model to trackthe respective identified object from which it was generated; anddetermining an amount of false identifications made by each respectivemodel of different objects in different video that does not include theobject from which it was generated.
 5. The method of claim 1 whereinidentifying at least one preferred model based on the evaluatingcomprises identifying one of the respective models having a greatestability to identify the other objects in the video that are similar tothe respective identified object from which the one of the respectivemodels was generated and having a least amount of false identificationsof different objects in different video that does not include therespective identified object from which it was generated.
 6. The methodof claim 1 wherein the identified objects are identified in the video bymanual identification.
 7. The method of claim 1 wherein the identifiedobjects are identified in the video by human head detection.
 8. Themethod of claim 1 wherein the identified objects identified in the videocomprise human body parts.
 9. A method of operating an image processingsystem, the method comprising: generating respective models fromidentified objects in a first video; evaluating an ability of eachrespective model to identify other objects from portions of said firstvideo when said other objects are similar to a respective identifiedobject from which said respective model was generated and distinguishdifferent objects from a different video when said different video doesnot include said other objects that are similar; and identifying atleast one preferred model based on the evaluating.
 10. The method ofclaim 9 further comprising directing a processing system to perform theevaluations on each respective model based on knowledge of therespective identified objects determined from video analysis and furtherdirecting the processing system to track a movement of each respectiveidentified object in the first video.
 11. The method of claim 10 furthercomprising directing the processing system to perform the evaluations oneach respective model based on knowledge of the identified objectsdetermined from video analysis and directing the processing system toevaluate an ability of each respective model to identify, from theportions of the first video, other objects in the first video that aresimilar to the identified object from which the respective model wasgenerated.
 12. The method of claim 10 further comprising directing theprocessing system to perform the evaluations on each respective modelbased on knowledge of the objects determined from video analysis andfurther directing the processing system to determine an amount of falseidentifications made by each respective model of said different objectsin the different video that does not include the identified object fromwhich the respective model was generated.
 13. The method of claim 10further comprising directing the processing system to perform theevaluations on each respective model based on knowledge of the objectsdetermined from video analysis and directing the processing system to:track movements of said identified objects in the first video by usingeach model to track a respective identified object from which arespective model was generated; evaluate an ability of said respectivemodel to identify the other objects in the video that are similar to theobject from which the respective model was generated; and determine anamount of false identifications made by each model of different objectsin the different video that does not include the object from which therespective model was generated.
 14. The method of claim 9 wherein theidentified objects are identified in the first video by manualidentification.
 15. The method of claim 9 wherein the identified objectsare identified in the first video by human head detection.
 16. An imageprocessing system comprising: a processing system configured to generaterespective models from identified objects in a first video, performevaluations on each model based on knowledge of the identified objectsdetermined from video analysis, and identify at least one preferredmodel based on the evaluations, wherein evaluating each model includesapplying a set of video analytics in order to effectuate the identifyingstep, and tracking a movement of each identified object in the firstvideo by using each respective model to track the respective identifiedobject from which the respective model was generated, evaluatingstatistics from the tracking based on the at least one preferred model;storing the statistics as a set of motion dynamics for the identifiedobject in a non-volatile computerized memory; identifying at least onescene in the first video by tracking the movement of a respectiveidentified object in the video; storing scene statistics in saidnon-volatile computerized memory.
 17. An image processing systemaccording to claim 16, wherein the scene statistics comprise at leastone of average movement step size of the first identified object,maximum movement step size of the first identified object, a rate ofspeed of the first identified object, physical location of the firstidentified object as depicted in the identified scene, and most usedtraffic pattern in an identified scene of said first identified object.18. An image processing system according to claim 16, further comprisinga video source transmitting a video stream of said first video, whereinthe processing system identifies at least one scene in the first videoby tracking the movement of an identified object in the video.
 19. Animage processing system according to claim 16, further comprising avideo source transmitting a video stream of said first video and adifferent video, wherein evaluating each model comprises evaluating arespective model for an ability to track a different object in thedifferent video when the different object is similar to the identifiedobject from the first video.
 20. An image processing system according toclaim 16, wherein an absence of at least one object in additionalportions of the first video is determined from the scene statisticsbased on the at least one preferred model.